Routing buses impact analysis on the results on modeling standard digital cell on CMOS 28 nm
Bibliographic record
Abstract
In this paper, the influence of routing buses on the timing characteristics (rise/fall time and switching delay) of standard digital elements due to the manifestation of LDE and parasitic effects was studied. A set of specialized test structures to take into account such effects in layers from the first to the fourth metal was proposed. The test structures provide some of the possible cases of the relative position of the routing buses and the layout of the standard cell. Parasitic extraction and characterization of the resulting netlist were performed for each test structure. A set of netlists with parasitic parameters was characterized. It is shown that the average deviation of the temporal characteristics ranged from 1.8 to 3.9% compared to the original structure without routing buses. The largest relative deviation in switching delay is typical for the smallest load capacity, while the relative deviation of cell characteristics depends relatively weakly on the front value. On the basis of the study, recommendations were formulated for modifying the route of extraction of parasitic parameters of standard digital elements, taking into account the routing buses, in order to increase the accuracy of their modeling.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".